Biometric data generally holds a wealth of unique identifying information that may be used in a variety of different ways, with security applications being a common use of biometric data. This is due, in part, to the fact that biometric data is not easily counterfeited and is uniquely associated with a person. Fingerprint and retina recognition devices, for example, are relatively common biometric data recognition devices that are used to collect biometric data for security purposes.
A common approach to fingerprint matching involves scanning a sample fingerprint or an image thereof and generating a ridge flow map for the fingerprint. Construction of the ridge flow map can include determining, for each cell in the ridge flow map, an angle of the ridge flow orientation. For example, in some instances the ridge flow orientation can be determined with respect to a horizontal axis. Some methods for matching fingerprints can require an estimation of the information content (entropy) of the ridge flow maps. A fingerprint match can be erroneous if the error in the entropy estimation is too great.
In other situations, pattern changes in a fingerprint may introduce correlated changes in the fingerprint, which can cause the ridge flow angle differences to become correlated. For example, the skin on a finger may become drier over time, and this increased dryness differences can result in a change in the fingerprint pattern. The correlated ridge flow angle differences can increase the probability that a fingerprint matching operation will fail.